Ad Revenue Forecasting – Current Models

This is the second post of a new series on online advertising revenue forecasting.

My first post (last week) identified a sampling of the various data points that need to be considered when building your own ad revenue model to better forecast. This time around we’ll look at the various methods out there to forecast advertising revenue for a website and why they aren’t so accurate.

The Guessing Game

First off, there’s the traditional guessing game. This is when a whole sales team gets in on the process and come up with an educated guess among them. They arrive at these guesses by looking at the past year’s sales per client and estimating how each will spend in the coming year – all based on actual personal relationships between seller and buyer. Sometimes this is based on discussions with the clients and can even include actual annual agreements.

The problem with this method, in my opinion, is that it relies too much on gut feelings and impressions and very little on hard facts. This is dangerous as the whole team will usually be in on it with the manager challenging everyone to come up with a better (higher) number – all will end up in agreement with a sales objective (expected revenue) they all “feel” is aggressive but achievable. This method is further “off” because it does not consider the probable growth of the website you’re forecasting for: neither natural growth, nor additions to the website that could generate either reach or traffic growth that can translate in to revenue growth down the line…

The Top-Down Method

This is where a company’s executive committee decide among themselves that a website they recently bought “NEEDS” to bring in $ XYZ over the next 5 years to be a profitable proposition. This usually makes a whole lot of sense from the CFO’s perspective – which also convinces the president.

Unfortunately, this is one solution that even the best arguments usually cannot win – because the president answers to a board of directors and oftentimes when the number’s been handed down to the sales team – it’s already been approved by the board… In any case, the problem with this method is it doesn’t consider current market situation, the actual ability of the team to achieve this target, the mathematical possibility of hitting this mark (is there sufficient inventory to begin with?) and more than a few other factors. Although “online media” has be around now for more than 15 years, that’s not enough for most senior executives to have a solid grasp on it and judge it appropriately.

The Market Average

Many publishers rely on industry associations such at the IAB in their country, eMarketer or other research aggregator to see where the market is going so they may apply the expected growth rate to this year’s expected final ad revenue figure as next year’s objective. For example, eMarketer predicts the US online ad sector to grow by 14.4% in 2012 over 2011 (which is also an estimate by the way). Similarly, here in Canada where I live we’re projecting a 16% growth rate for 2011 over 2010’s actuals.

The problem with this is twofold. First off, the projected growth rate must be split among the key constituents of the annual totals which include display advertising, search advertising, video, email, lead generation, classifieds and directories. Each of these has its own growth rate; however these numbers are not always available for future projections. Here in Canada we publish the detailed data on the previous year’s actuals, but not for the projected current year’s figure. You must then estimate yourself if display for example will grow faster than search or vice versa. Secondly, this is for the market average growth which takes into account that many larger player will actually grow by a slower rate than that average while many smaller players will achieve much higher growth rates. Every month new websites crop up, among the major players and among independents, and they all seek out their share of the total ad pie.

The eCPM Linear Growth Method

The eCPM is an interesting metric to look back on, to see how much your total inventory sold for per average thousand impressions. But planning based on this metric multiplied by an expected future inventory is like licking your finger and raising it to decide at which precise degree on a compass the wind is blowing: it gives a good enough indicator of direction, but over the long haul can actually take you way off course. Your website’s traffic can actually grow leaps and bounds, or shrink while revenues could remain flat, where would your past eCPM have led you then?

In my next post I’ll look at the required information to properly and objectively forecast your online ad sales – regardless how big or small you are.

This post was previously published on Samuel Parent’s blog on January 13th, 2012

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